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Accelerating DSP Algorithms Using FPGAs

Accelerating DSP Algorithms Using FPGAs. Sean Gallagher DSP Specialist Xilinx Inc. Why DSP in FPGAs. Availability of fast analog-to-digital converters (ADCs) Enables digital methods for functions traditionally done in RF components Massive parallel processing

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Accelerating DSP Algorithms Using FPGAs

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  1. Accelerating DSP Algorithms Using FPGAs Sean Gallagher DSP Specialist Xilinx Inc

  2. Why DSP in FPGAs • Availability of fast analog-to-digital converters (ADCs) • Enables digital methods for functions traditionally done in RF components • Massive parallel processing • FPGAs may have several hundred embedded multipliers on-chip • One FPGA can replace many DSP Processors

  3. Architectural Considerations • FPGA architectures are vendor specific • Unlike ASICS, no two are alike • FPGA vendors develop distinct competencies • In device architecture design • In intellectual property (dsp functions, bus controllers, etc) • In design tool flows • Vendor independent HDL can be written but this usually achieves mediocre results in clock speed and design size instantiation

  4. FPGAs Are Massive Parallel Computing Machines 80MHz Samples ch1 20MHz Samples LPF • FPGAs are ideally suited for multi-channel DSP designs • Many low sample rate channels can be multiplexed (e.g. TDM) and processed in the FPGA, at a high rate • Interpolation (using zeros) can also drive sample rates higher ch2 LPF LPF ch3 LPF Multi Channel Filter ch4 LPF

  5. × × × × + + + + + + FPGAs Allow Space/Speed Trade-offs A Q = (A x B) + (C x D) + (E x F) + (G x H) can be implemented in parallel B C D Q E F G H But is this the only way in the FPGA?

  6. × × × × × × × D Q + + + + + + + + + + + + Customize Architectures to Suit your Ideal Algorithms FPGAs allow Area (cost) / Performance tradeoffs Parallel Semi-Parallel Serial D Q Speed Optimized for? Area

  7. Exploitng The Xilinx Architecture For DSP Functions • Memory Blocks that can be configured as ROMs, dual port RAMs, FIFOs • Embedded 18x18 multipliers that can be ganged to form a 35x35 bit multiply • SRL16 shift registers • A patented technique for turning the 4 input lookup table (2 per slice) into an addressable shift register

  8. Using SRL16E to increase Compute Density 20MHz 4 channels 9 9 9 k3 k2 k1 k0 9 18 + + + + ‘0’ 9 SRL16E takes the same area as one LUT. It can be used for up to 16 channels. 9 channels 9 20MHz k3 k2 + + ‘0’

  9. Xilinx System Generator For DSP • System Generator is a Block Set that resides in Simulink/Matlab environment. • System Generator blocks are bit true and cycle true models of Xilinx’s DSP intellectual property (IP) cores. • Hardware DSP design capture is significantly accelerated due to automatic code generation from Simulink

  10. Algorithm Instantiation Considerations • There are cases where following a textbook approach does not necessarily translate into an efficient instantiation • Manipulating the algorithm to exploit features of the architecture can lead to much more efficient instantiations • Modification of a text book algorithm includes how the math is executed as well as over-clocking structures to allow the structures to be time division multiplexed

  11. Example 1: Digital Down Conversion • In digital down conversion we need to filter before we decimate to prevent aliasing • These filters can get rather large because the transition band is rather narrow in relation to the sample rate • A text book solution is to step the sample rate down in steps

  12. Digital Down Conversion • The following 3 slides show three different filter designs for the down conversion of a .625 Mhz band of interest that is centered at 20 MHz and sampled at 61.44 MHz. • The decimation rate is 25 • The final sample rate will be 61.44/25= 2.4576MHz • The next slide shows the filter design needed if decimating by 25 in one step • the total coefficient count is 184 • The two slides after the next show the two filters necessary to decimate in steps, decimating by 5 in each step • The total coefficient count is 11+43=54

  13. Digital Down Conversion (DDC) Implementation • The following design shows how the DDC function would be implemented using the FIR filter core from the Xilinx Library • The coefficients are automatically loaded into the filter cores • The design has been compiled and was found to use about 6000 logic slices • The fir filter core is a legacy core and is built as an optimized lookup table of coefficients

  14. Digital Down Conversion Implementation

  15. DDC –Another Way • While we were able to exploit the math of DSP to reduce our coefficient count, we did not necessarily exploit the Xilinx architecture. • The next design shows a design that implements the 184 coefficient filter but is significantly smaller in instantiation size then the previous design • This design exploits the memory, embedded multipliers, and SRL16s

  16. Time Division Multiplexed Input Multiplexing I&Q multiplication so that just one filter is needed instead of two

  17. Efficient Shift Registers via SRL16s Delay line would require 16x50x7=5200 registers which would be 2800 logic slices. Use of SRL16s reduces slice count to less then 700

  18. Clock Based Demuxing And Automatic Pipeline Balancing Down sample block grabs last sample in a frame Delay block “slide” frame Balancing latencies is a common requirement in DSP designs. The Sync block uses SRL16s (very efficient) to automatically balance pipeline delays Down sample block grabs next sample in a frame

  19. Notes on Previous Design • One filter structure is used by clocking the filter at twice the rate of the incoming data • The coefficients are stored in memory, 25 per rom. There are 200 coefficients but this approach allows storage of many more • The delay between taps is built using SRL 16s. This would have taken 2800 slices alone without SRL16s but instead the entire design is less that 700 slices

  20. Channelizer Design • The following design is a 64 channel channelizer based on the technique known as polyphase decimation filter with a DFT bank • The design basebands and decimates 64 channels simultaniously • The polyphase decimation is the same structure as the previous design, hence very efficient device utilization. • This filter structure uses the on-chip ram blocks of the Xilinx device to store the coefficients • This technique requires a tapped shift register that requires 6272 registers (3136 slices). However, Xilinx’s patented ability to turn the logic look-up table into a 16 bit register reduces this require by more than an order of magnitude. The whole design is less than 1700 slices. • The DFT is implemented with a streaming fft core. The streaming mode allows the FFT to keep up with the data rate • Individual channels out of the fft are demuxed using the implied clocking technique seen in the previous design

  21. 512 Coefficients are stored in on chip block rams 64 pt FFT set to streaming mode

  22. Filter coefficients are stored in on-chip block rams. A new phase of the 64 phase-polyphase filter is rotated into the multipliers on every clock cycle. There are 64 phases x 8 taps =512 coefficients

  23. Conclusion • Efficient FPGA instantiation of DSP algorithms requires exploitation of the FPGA vendor’s architecture. Xilinx’s Virtex II architecture is especially amenable to systolic computation structures • FPGA architectures may present non-obvious instantiation choices that are more efficient then a typical textbook approach • Algorithms can and should be modified for parallelized data flow instantiation.

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